%0 Journal Article %1 journals/jstat/scutari2010 %A Scutari, Marco %D 2010 %J Journal of Statistical Software %K bayesian bnlearn learning networks %N 3 %P 1-22 %T Learning Bayesian Networks with the bnlearn R Package %U http://arxiv.org/abs/0908.3817 %V 35 %X bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package.

@article{journals/jstat/scutari2010, abstract = {bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package.}, added-at = {2015-02-11T15:12:33.000+0100}, author = {Scutari, Marco}, biburl = {https://www.bibsonomy.org/bibtex/2229995b6ebb969375abcb81b6fe45965/mgasse}, description = {Learning Bayesian Networks with the bnlearn R Package}, interhash = {ee1a164155cab591ec9371045e35a762}, intrahash = {229995b6ebb969375abcb81b6fe45965}, journal = {Journal of Statistical Software}, keywords = {bayesian bnlearn learning networks}, month = jul, number = 3, pages = {1-22}, timestamp = {2015-02-13T16:24:01.000+0100}, title = {Learning Bayesian Networks with the bnlearn R Package}, url = {http://arxiv.org/abs/0908.3817}, volume = 35, year = 2010 }